NEWSFLOWS
Financiado
Modeling news flows How feedback loops influence citizens beliefs and shape so...
"Our beliefs about society are largely based on information that we encounter through media. This happens more and more in an ""unbundled form: Single news items are distributed through sharing on social media, sorted by algorithm...
"Our beliefs about society are largely based on information that we encounter through media. This happens more and more in an ""unbundled form: Single news items are distributed through sharing on social media, sorted by algorithms, and encountered on platforms on which they were not originally published. Many argue that this leads to so-called echo chambers and filter bubbles, communities of people that are only exposed to information they agree with. This is thought to lead to increasing polarization of society, and to a lack of diversity in people’s (virtual) communities.
But a growing body of evidence suggests that these metaphors are misleading.
In fact, as recent discussions on so-called fake news illustrate, biased and/or extreme information is not locked up in filter bubbles or echo chambers, but spreads from niche communities into mainstream media and politics. NEWSFLOWS develops an alternative model of how information spreads in today’s media ecosystem – a model based on so-called feedback loops, which are essential for the modern complex system of information flows. To give an example of a feedback loop: If a news item receives many shares on social media, this may let a recommendation algorithm show it to even more users (and journalists and politicans), making it more likely that they will act on it, again increasing the number of shares, etc. Crucially, neither the algorithm, nor the users, nor the writers alone determine the eventual spread, but a combination of their influences and feedback loops.
Theoretical models and empirical methods to study such feedback loops in the social sciences and humanities are scarce. NEWSFLOWS extends innovative methods as online field experiments, data donations, and automated content analysis to conduct such studies. This will greatly enhance the theoretical understanding of news flows, but also enable media organizations to develop products conforming to calls for ""responsible AI""."
ver más
Duración del proyecto: 61 meses
Fecha Inicio: 2020-11-30
Fecha Fin: 2025-12-31
Fecha Fin: 2025-12-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2020-11-30
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2020-STG:
ERC STARTING GRANTS
Cerrada
hace 5 años
Presupuesto
El presupuesto total del proyecto asciende a
1M€
Líder del proyecto
STICHTING VU
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
503.46K€ |
Participante